Question

我已经在tensorflow中编写了以下快速程序来打印斐波那契数。初始fib编号初始化为占位符x1和x2，但是当我尝试在session.run中填充占位符的值时，会导致错误：

InvalidArgumentError: You must feed a value for placeholder tensor 'x2' with dtype int64 and shape [1]

您能帮助我理解并解决代码问题吗？

import tensorflow as tf
session = tf.InteractiveSession()
import numpy as np

ones = tf.ones((1,))

n = 10
x1 = tf.placeholder(tf.int64, [1], name='x1')
x2 = tf.placeholder(tf.int64, [1], name='x2')
temp = tf.Variable(ones, name='temp')

tf.initialize_all_variables().run()
fib_nums = [x1, x2]
for i in range(100):
  temp = x1 + x2
  x1 = x2
  x2 = temp
  fib_nums.append(temp)

series = tf.stack(fib_nums)
print(np.ones(1).astype(np.int64))
session.run(series, feed_dict={x1:np.ones(1).astype(np.int64), x2:np.ones(1).astype(np.int64)})
print(series.eval())

Answer 1

这里有两个错误。首先，由于您正在重用Python名称x1和x2，因此当您在feed_dict中使用它们时，它们不再引用占位符，而是引用循环的最后结果。因此，您应该更改代码，以便在feed_dict中输入的键确实是占位符。其次，您先使用正确的session.run调用feed_dict，然后再调用series.eval()，它与上一行基本相同，只是您没有提供{{ 1}}，因此无法正常工作。您实际上并不需要调用feed_dict，只需取series.eval()返回的值即可。您的固定程序可能如下所示：

session.run

输出：

import tensorflow as tf
session = tf.InteractiveSession()
import numpy as np

ones = tf.ones((1,))

n = 10
# Reserve these Python names for the placeholders
x1_ph = tf.placeholder(tf.int64, [1], name='x1')
x2_ph = tf.placeholder(tf.int64, [1], name='x2')
temp = tf.Variable(ones, name='temp')

tf.initialize_all_variables().run()
# Use placeholders as initial values in the iterations
x1, x2 = x1_ph, x2_ph
fib_nums = [x1, x2]
for i in range(100):
  temp = x1 + x2
  x1 = x2
  x2 = temp
  fib_nums.append(temp)

series = tf.stack(fib_nums)
print(np.ones(1).astype(np.int64))
# You can just give lists as inputs and TensorFlow will convert their type
series_val = sess.run(series, feed_dict={x1_ph: [1], x2_ph: [1]})
print(series_val)

Answer 2

我认为使用tf.identity的另一种变化形式。

y = tf.identity(x1)
y1 = tf.identity(x2)
fib_nums = [y, y1]
for i in range(100):
  temp = y + y1
  y = y1
  y1 = temp
  fib_nums.append(temp)

此占位符修改问题也在here中进行了讨论

这是获得系列的另一种方法。

def cond(i, x_next, x_prev):
    return x_next <= 100

def body( i, x_next, x_prev ):

    nextinseries = x_next + x_prev

    next = tf.Print(x_next, [x_next], message="Next is : ")
    prev = tf.Print(x_prev, [x_prev], message="Previous is : ")

    with tf.control_dependencies([nextinseries]):
        prev = tf.identity( next )


    return [i + 1, nextinseries , prev ]

sess = tf.Session()
sess.run(tf.global_variables_initializer())


sess.run(tf.while_loop(cond, body, [1, 1, 1]))

Answer 3

只需将tf.placeholder更改为张量即可使用

x1 = tf.ones(dtype=tf.int64, shape=1, name='x1') 
x2 = tf.ones(dtype=tf.int64, shape=1, name='x2')

如何在Tensorflow中正确设置占位符的值？

3 个答案: